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Lumbar and Thoracic Spine Segmentation Using a Statistical Multi-object Shape\(+\)Pose Model

  • A. SeitelEmail author
  • A. RasoulianEmail author
  • R. Rohling
  • P. Abolmaesumi
Chapter
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 20)

Abstract

The vertebral column is of particular importance for many clinical procedures such as anesthesia or anaelgesia. One of the main challenges for diagnostic and interventional tasks at the spine is its robust and accurate segmentation. There exist a number of segmentation approaches that mostly perform segmentation on the individual vertebrae. We present a novel segmentation approach that uses statistical multi-object shape\(+\)pose models and evaluate it on a standardized data set. We could achieve a mean dice coefficient of \(0.83\) for the segmentation. The flexibility of our approach let it become valuable for the specific segmentation challenges in clinical routine.

Keywords

Spine Segmentation Mean Dice Coefficient Individual Vertebrae Interventional Tasks Appearance Model Approach 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work was funded by the CIHR.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringUniversity of British Columbia (UBC)VancouverCanada
  2. 2.Department of Mechanical EngineeringUBCVancouverCanada

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